Kernel PCA for Type Ia supernovae photometric classification

@article{Ishida2012KernelPF,
  title={Kernel PCA for Type Ia supernovae photometric classification},
  author={Emille E. O. Ishida and Rafael S. de Souza},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2012},
  volume={430},
  pages={509-532}
}
  • E. IshidaR. Souza
  • Published 31 January 2012
  • Physics, Computer Science
  • Monthly Notices of the Royal Astronomical Society
The problem of supernova photometric identication will be extremely important for large surveys in the next decade. In this work, we propose the use of Kernel Principal Component Analysis (KPCA) combined with k = 1 nearest neighbour algorithm (1NN) as a framework for supernovae (SNe) photometric classication. 

Improved KPCA for supernova photometric classification

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Photometric classification of Supernovae from the SUDARE survey

  • G. Pignata
  • Physics
    Proceedings of the International Astronomical Union
  • 2014
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References

SHOWING 1-10 OF 39 REFERENCES

Bayesian Single-Epoch Photometric Classification of Supernovae

Ongoing supernova (SN) surveys find hundreds of candidates that require confirmation for their various uses. Traditional classification based on follow-up spectroscopy of all candidates is virtually

A Probabilistic Approach to Classifying Supernovae Using Photometric Information

This paper presents a novel method for determining the probability that a supernova candidate belongs to a known supernova type (such as Ia, Ibc, IIL, etc.) using its photometric information alone.

Semi-supervised learning for photometric supernova classification★

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the non-linear dimension reduction technique diffusion map to detect structure in a data base of

Statistical classification techniques for photometric supernova typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on light curves

Photometric Identification of Type Ia Supernovae at Moderate Redshift

Large photometric surveys with the aim of identifying many Type Ia supernovae (SNe) at moderate redshift are challenged in separating these SNe from other SN types. We are motivated to identify Type

Photometric Selection of High-Redshift Type Ia Supernova Candidates

We present a method for selecting high-redshift Type Ia supernovae (SNe Ia) located via rolling SN searches. The technique, using both color and magnitude information of events from only two to three

PHOTOMETRIC TYPE Ia SUPERNOVA CANDIDATES FROM THE THREE-YEAR SDSS-II SN SURVEY DATA

We analyze the three-year Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey data and identify a sample of 1070 photometric Type Ia supernova (SN Ia) candidates based on their multiband

A simple and robust method for automated photometric classification of supernovae using neural networks

A method is presented for automated photometric classificat ion of supernovae (SNe) as TypeIa or non-Ia. A two-step approach is adopted in which: (i) the SN lightcurve flux measurements in each

Results from the Supernova Photometric Classification Challenge

We report results from the Supernova Photometric Classification Challenge (SNPhotCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to

COSMOLOGY WITH PHOTOMETRIC SURVEYS OF TYPE Ia SUPERNOVAE

We discuss the extent to which photometric measurements alone can be used to identify Type Ia supernovae (SNIa) and to determine the redshift and other parameters of interest for cosmological